Shape Optimization With Surface-Mapped CPPNs

Daniel Richards, Martyn Amos

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
1 Downloads (Pure)

Abstract

Shape optimization techniques are becoming increasingly important in design and engineering. This growing significance reflects the need to exploit advances in digital fabrication technologies, and the desire to create new types of surface designs for various engineering applications. Evolutionary algorithms (EAs) offer several key advantages for shape optimization, but they can also be restricted, especially as design problems scale up in size. A key challenge for evolutionary shape optimization is to overcome these challenges in order to apply EAs to large-scale, “real-world” engineering problems. This paper presents a new evolutionary approach to shape optimization using what we call “surface-mapped compositional pattern producing networks (CPPNs).” Our method outperforms a state-of-the-art gradient-based method on a simple benchmark problem, and scales well as degrees of freedom are added to the design problem. Our results demonstrate that surface-mapped CPPNs offer practical ways of approaching large-scale, real-world engineering problems with EAs, opening up exciting new opportunities for engineering design.
Original languageEnglish
Pages (from-to)391-407
JournalIEEE Transactions on Evolutionary Computation
Volume21
Issue number3
Early online date2 Sep 2016
DOIs
Publication statusPublished - Jun 2017
Externally publishedYes

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